Description
Machine learning based detection models can strengthen detection, but there remain some significant barriers to the widespread deployment of such techniques in operational detection systems. In this presentation, we identify the main challenges to overcome and we provide both methodological guidance and practical solutions to address them. The solutions we present are completely generic to be beneficial to any detection problem on any data type and are freely available in SecuML.The content of the presentation is mostly based on my PhD thesis “Expert-in-the-Loop Supervised Learning for Computer Security Detection Systems”.
Infos pratiques
Prochains exposés
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Les jeux vidéo de l’écran au réel : enjeux juridiques et (géo)politiques au prisme de la cybersécurité
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Protection des droits d’auteur, lutte contre les techniques de triche, interactions avec la guerre et les conflits hybrides, enjeux de démocratie ... Sous l’angle de la cybersécurité les enjeux juridiques et (géo)politiques des jeux video sont nombreux. Cette présentation du groupe de travail sur les jeux video (GTJV) permettra d’alimenter la réflexion sur l’articulation entre jeux video et[…]-
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The Quest for my Perfect MATE. Investigate MATE: Man-at-the-End attacker (followed by a hands-on application).
Orateur : Mohamed Sabt, Etienne Nedjaï - Univ Rennes, IRISA
Shannon sought security against an attacker with unlimited computational powers: if an information source conveys some information, then Shannon’s attacker will surely extract that information. Diffie and Hellman refined Shannon’s attacker model by taking into account the fact that the real attackers are computationally limited. This idea became one of the greatest new paradigms in computer[…]